Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1537
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory912.8 KiB
Average record size in memory608.2 B

Variable types

Categorical6
Numeric8
URL1
Unsupported1
Boolean5

Alerts

is_skinless has constant value "False" Constant
is_headless has constant value "False" Constant
Color is highly overall correlated with Color_encoded and 1 other fieldsHigh correlation
Color_encoded is highly overall correlated with Color and 1 other fieldsHigh correlation
Defect is highly overall correlated with Color and 4 other fieldsHigh correlation
PCA1 is highly overall correlated with Total Length (cm) and 4 other fieldsHigh correlation
PCA2 is highly overall correlated with Total Length (cm) and 1 other fieldsHigh correlation
Species is highly overall correlated with Defect and 1 other fieldsHigh correlation
Species_encoded is highly overall correlated with Defect and 1 other fieldsHigh correlation
Total Length (cm) is highly overall correlated with PCA1 and 5 other fieldsHigh correlation
Total Length (cm)_is_outlier is highly overall correlated with PCA1 and 5 other fieldsHigh correlation
Total Length (cm)_zscore is highly overall correlated with Total Length (cm)_is_outlier and 3 other fieldsHigh correlation
Total Weight (g) is highly overall correlated with PCA1 and 4 other fieldsHigh correlation
Total Weight (g)_is_outlier is highly overall correlated with PCA1 and 7 other fieldsHigh correlation
Total Weight (g)_zscore is highly overall correlated with Total Length (cm)_is_outlier and 3 other fieldsHigh correlation
num_defects is highly overall correlated with DefectHigh correlation
weight_per_cm is highly overall correlated with PCA1 and 4 other fieldsHigh correlation
weight_per_cm_is_outlier is highly overall correlated with PCA2 and 2 other fieldsHigh correlation
weight_per_cm_zscore is highly overall correlated with Total Length (cm)_zscore and 3 other fieldsHigh correlation
Color is highly imbalanced (68.3%) Imbalance
Species is highly imbalanced (94.3%) Imbalance
Defect is highly imbalanced (60.0%) Imbalance
num_defects is highly imbalanced (64.8%) Imbalance
Color_encoded is highly imbalanced (68.3%) Imbalance
Species_encoded is highly imbalanced (94.3%) Imbalance
Total Weight (g)_is_outlier is highly imbalanced (87.2%) Imbalance
Total Length (cm)_is_outlier is highly imbalanced (93.0%) Imbalance
weight_per_cm_is_outlier is highly imbalanced (90.4%) Imbalance
Defect_List is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-06-18 18:23:12.096643
Analysis finished2025-06-18 18:23:21.606672
Duration9.51 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Color
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size94.5 KiB
Medium
1407 
Light
 
70
Dark
 
60

Length

Max length6
Median length6
Mean length5.8763826
Min length4

Characters and Unicode

Total characters9032
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowDark
3rd rowMedium
4th rowDark
5th rowDark

Common Values

ValueCountFrequency (%)
Medium 1407
91.5%
Light 70
 
4.6%
Dark 60
 
3.9%

Length

2025-06-18T18:23:21.687049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T18:23:21.769247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 1407
91.5%
light 70
 
4.6%
dark 60
 
3.9%

Most occurring characters

ValueCountFrequency (%)
i 1477
16.4%
M 1407
15.6%
e 1407
15.6%
d 1407
15.6%
u 1407
15.6%
m 1407
15.6%
L 70
 
0.8%
g 70
 
0.8%
h 70
 
0.8%
t 70
 
0.8%
Other values (4) 240
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1477
16.4%
M 1407
15.6%
e 1407
15.6%
d 1407
15.6%
u 1407
15.6%
m 1407
15.6%
L 70
 
0.8%
g 70
 
0.8%
h 70
 
0.8%
t 70
 
0.8%
Other values (4) 240
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1477
16.4%
M 1407
15.6%
e 1407
15.6%
d 1407
15.6%
u 1407
15.6%
m 1407
15.6%
L 70
 
0.8%
g 70
 
0.8%
h 70
 
0.8%
t 70
 
0.8%
Other values (4) 240
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1477
16.4%
M 1407
15.6%
e 1407
15.6%
d 1407
15.6%
u 1407
15.6%
m 1407
15.6%
L 70
 
0.8%
g 70
 
0.8%
h 70
 
0.8%
t 70
 
0.8%
Other values (4) 240
 
2.7%

Species
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size94.7 KiB
Loligo
1527 
lllex
 
10

Length

Max length6
Median length6
Mean length5.9934938
Min length5

Characters and Unicode

Total characters9212
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoligo
2nd rowLoligo
3rd rowLoligo
4th rowLoligo
5th rowLoligo

Common Values

ValueCountFrequency (%)
Loligo 1527
99.3%
lllex 10
 
0.7%

Length

2025-06-18T18:23:21.862728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T18:23:21.932413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
loligo 1527
99.3%
lllex 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 3054
33.2%
l 1557
16.9%
L 1527
16.6%
i 1527
16.6%
g 1527
16.6%
e 10
 
0.1%
x 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3054
33.2%
l 1557
16.9%
L 1527
16.6%
i 1527
16.6%
g 1527
16.6%
e 10
 
0.1%
x 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3054
33.2%
l 1557
16.9%
L 1527
16.6%
i 1527
16.6%
g 1527
16.6%
e 10
 
0.1%
x 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3054
33.2%
l 1557
16.9%
L 1527
16.6%
i 1527
16.6%
g 1527
16.6%
e 10
 
0.1%
x 10
 
0.1%

Total Length (cm)
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.507742
Minimum5
Maximum24.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-06-18T18:23:22.277819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q19
median10
Q312
95-th percentile15
Maximum24.2
Range19.2
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5445123
Coefficient of variation (CV)0.24215595
Kurtosis1.9081611
Mean10.507742
Median Absolute Deviation (MAD)2
Skewness0.70059158
Sum16150.4
Variance6.4745429
MonotonicityNot monotonic
2025-06-18T18:23:22.460943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 275
17.9%
11 220
14.3%
12 178
11.6%
9 164
10.7%
8 151
9.8%
13 100
 
6.5%
7 95
 
6.2%
14 55
 
3.6%
15 40
 
2.6%
6 38
 
2.5%
Other values (69) 221
14.4%
ValueCountFrequency (%)
5 17
 
1.1%
5.1 1
 
0.1%
5.5 1
 
0.1%
6 38
 
2.5%
6.1 1
 
0.1%
6.2 3
 
0.2%
6.3 2
 
0.1%
6.5 7
 
0.5%
7 95
6.2%
7.1 1
 
0.1%
ValueCountFrequency (%)
24.2 1
0.1%
23.2 2
0.1%
22 1
0.1%
21.5 1
0.1%
21 2
0.1%
20.2 1
0.1%
19.5 2
0.1%
18.8 1
0.1%
18.5 2
0.1%
18 2
0.1%

Total Weight (g)
Real number (ℝ)

High correlation 

Distinct138
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.869876
Minimum4
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-06-18T18:23:22.611466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q132
median46
Q364
95-th percentile104
Maximum196
Range192
Interquartile range (IQR)32

Descriptive statistics

Standard deviation27.953877
Coefficient of variation (CV)0.5495173
Kurtosis3.2520016
Mean50.869876
Median Absolute Deviation (MAD)15
Skewness1.414394
Sum78187
Variance781.41925
MonotonicityNot monotonic
2025-06-18T18:23:22.766329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 63
 
4.1%
42 61
 
4.0%
44 60
 
3.9%
40 58
 
3.8%
38 48
 
3.1%
54 47
 
3.1%
36 47
 
3.1%
46 44
 
2.9%
32 44
 
2.9%
48 43
 
2.8%
Other values (128) 1022
66.5%
ValueCountFrequency (%)
4 2
 
0.1%
5 1
 
0.1%
6 9
0.6%
7 1
 
0.1%
8 8
0.5%
9 3
 
0.2%
10 10
0.7%
11 2
 
0.1%
12 19
1.2%
13 3
 
0.2%
ValueCountFrequency (%)
196 1
 
0.1%
192 1
 
0.1%
179 1
 
0.1%
173 2
0.1%
172 4
0.3%
171 1
 
0.1%
162 2
0.1%
160 1
 
0.1%
156 2
0.1%
150 1
 
0.1%

Defect
Categorical

High correlation  Imbalance 

Distinct32
Distinct (%)2.1%
Missing2
Missing (%)0.1%
Memory size113.3 KiB
["Whole","Skinless"]
991 
["Whole"]
152 
['Whole']
146 
["Headless","Skinless"]
 
63
['Whole', 'Skinless']
 
62
Other values (27)
121 

Length

Max length140
Median length20
Mean length18.407166
Min length9

Characters and Unicode

Total characters28255
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)1.0%

Sample

1st row['Skinless', 'Headless']
2nd row['Headless']
3rd row['Whole', 'Skinless']
4th row['Whole']
5th row['Skinless', 'Headless']

Common Values

ValueCountFrequency (%)
["Whole","Skinless"] 991
64.5%
["Whole"] 152
 
9.9%
['Whole'] 146
 
9.5%
["Headless","Skinless"] 63
 
4.1%
['Whole', 'Skinless'] 62
 
4.0%
["Skinless","Headless"] 42
 
2.7%
['Headless'] 16
 
1.0%
['Headless', 'Skinless'] 15
 
1.0%
["Skinless","Whole"] 9
 
0.6%
['Whole', 'Headless'] 5
 
0.3%
Other values (22) 34
 
2.2%

Length

2025-06-18T18:23:22.939056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
whole","skinless 991
58.9%
whole 375
 
22.3%
skinless 93
 
5.5%
headless","skinless 63
 
3.7%
headless 55
 
3.3%
skinless","headless 42
 
2.5%
tube 18
 
1.1%
partial 17
 
1.0%
skinless","whole 9
 
0.5%
name 5
 
0.3%
Other values (7) 15
 
0.9%

Most occurring characters

ValueCountFrequency (%)
" 4758
16.8%
e 2950
10.4%
l 2763
9.8%
s 2730
9.7%
[ 1535
 
5.4%
] 1535
 
5.4%
W 1380
 
4.9%
o 1380
 
4.9%
h 1380
 
4.9%
, 1233
 
4.4%
Other values (33) 6611
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 4758
16.8%
e 2950
10.4%
l 2763
9.8%
s 2730
9.7%
[ 1535
 
5.4%
] 1535
 
5.4%
W 1380
 
4.9%
o 1380
 
4.9%
h 1380
 
4.9%
, 1233
 
4.4%
Other values (33) 6611
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 4758
16.8%
e 2950
10.4%
l 2763
9.8%
s 2730
9.7%
[ 1535
 
5.4%
] 1535
 
5.4%
W 1380
 
4.9%
o 1380
 
4.9%
h 1380
 
4.9%
, 1233
 
4.4%
Other values (33) 6611
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 4758
16.8%
e 2950
10.4%
l 2763
9.8%
s 2730
9.7%
[ 1535
 
5.4%
] 1535
 
5.4%
W 1380
 
4.9%
o 1380
 
4.9%
h 1380
 
4.9%
, 1233
 
4.4%
Other values (33) 6611
23.4%

Image
URL

Distinct1534
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size289.2 KiB
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/2/tallyvision_qc/undefined.jpg
 
4
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/cfe5b6f2-d555-4ca8-818c-4fcbb0970b6e.jpg
 
1
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/7ce32d9c-9923-4785-9e43-0526220727a6.jpg
 
1
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/419ea994-070a-4b6c-af46-5c5927ab867a.jpg
 
1
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/c95dabb7-0971-4619-ad98-49a5f19911c0.jpg
 
1
Other values (1529)
1529 
ValueCountFrequency (%)
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/2/tallyvision_qc/undefined.jpg 4
 
0.3%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/cfe5b6f2-d555-4ca8-818c-4fcbb0970b6e.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/7ce32d9c-9923-4785-9e43-0526220727a6.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/419ea994-070a-4b6c-af46-5c5927ab867a.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/c95dabb7-0971-4619-ad98-49a5f19911c0.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/b43d4abd-87f9-43af-afe5-e7c024f71ed1.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/7cbebe5c-79de-416f-a915-5367f9e0bca8.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/a9c79139-7d27-4433-9e79-0ad9d794576c.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/46113a71-61e2-47a4-ad64-d2bcd3c3950c.jpg 1
 
0.1%
https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/f587c156-29fc-4d99-aa82-b641a904ea27.jpg 1
 
0.1%
Other values (1524) 1524
99.2%
ValueCountFrequency (%)
https 1537
100.0%
ValueCountFrequency (%)
storage.googleapis.com 1537
100.0%
ValueCountFrequency (%)
/upload-raw-images/tallyvision-camera-08/2025/4/2/tallyvision_qc/undefined.jpg 4
 
0.3%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/cfe5b6f2-d555-4ca8-818c-4fcbb0970b6e.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/7ce32d9c-9923-4785-9e43-0526220727a6.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/419ea994-070a-4b6c-af46-5c5927ab867a.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/c95dabb7-0971-4619-ad98-49a5f19911c0.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/b43d4abd-87f9-43af-afe5-e7c024f71ed1.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/7cbebe5c-79de-416f-a915-5367f9e0bca8.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/a9c79139-7d27-4433-9e79-0ad9d794576c.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/46113a71-61e2-47a4-ad64-d2bcd3c3950c.jpg 1
 
0.1%
/upload-raw-images/tallyvision-camera-08/2025/4/14/tallyvision_qc/f587c156-29fc-4d99-aa82-b641a904ea27.jpg 1
 
0.1%
Other values (1524) 1524
99.2%
ValueCountFrequency (%)
1537
100.0%
ValueCountFrequency (%)
1537
100.0%

Defect_List
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size182.1 KiB

num_defects
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.2 KiB
2
1203 
1
320 
3
 
11
0
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1537
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 1203
78.3%
1 320
 
20.8%
3 11
 
0.7%
0 2
 
0.1%
4 1
 
0.1%

Length

2025-06-18T18:23:23.053646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T18:23:23.134222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1203
78.3%
1 320
 
20.8%
3 11
 
0.7%
0 2
 
0.1%
4 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 1203
78.3%
1 320
 
20.8%
3 11
 
0.7%
0 2
 
0.1%
4 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1203
78.3%
1 320
 
20.8%
3 11
 
0.7%
0 2
 
0.1%
4 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1203
78.3%
1 320
 
20.8%
3 11
 
0.7%
0 2
 
0.1%
4 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1203
78.3%
1 320
 
20.8%
3 11
 
0.7%
0 2
 
0.1%
4 1
 
0.1%

is_skinless
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1537 
ValueCountFrequency (%)
False 1537
100.0%
2025-06-18T18:23:23.195209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

is_headless
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1537 
ValueCountFrequency (%)
False 1537
100.0%
2025-06-18T18:23:23.238892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

weight_per_cm
Real number (ℝ)

High correlation 

Distinct377
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5713815
Minimum0.78947368
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-06-18T18:23:23.327863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.78947368
5-th percentile2.2785714
Q13.6
median4.4
Q35.3043478
95-th percentile7.3180952
Maximum18
Range17.210526
Interquartile range (IQR)1.7043478

Descriptive statistics

Standard deviation1.6572339
Coefficient of variation (CV)0.36252364
Kurtosis7.337887
Mean4.5713815
Median Absolute Deviation (MAD)0.84444444
Skewness1.5482842
Sum7026.2134
Variance2.7464241
MonotonicityNot monotonic
2025-06-18T18:23:23.473044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 90
 
5.9%
5 44
 
2.9%
4.2 43
 
2.8%
4.4 42
 
2.7%
4.545454545 32
 
2.1%
6 31
 
2.0%
3 29
 
1.9%
3.25 29
 
1.9%
4.909090909 29
 
1.9%
4.666666667 25
 
1.6%
Other values (367) 1143
74.4%
ValueCountFrequency (%)
0.7894736842 1
 
0.1%
0.8 2
 
0.1%
0.9090909091 1
 
0.1%
1 2
 
0.1%
1.066666667 1
 
0.1%
1.076923077 1
 
0.1%
1.2 7
0.5%
1.230769231 2
 
0.1%
1.333333333 4
0.3%
1.384615385 2
 
0.1%
ValueCountFrequency (%)
18 1
0.1%
15.14285714 1
0.1%
15 1
0.1%
14.81481481 1
0.1%
13 1
0.1%
12 1
0.1%
11.8 1
0.1%
11.6 1
0.1%
11.33333333 2
0.1%
11.2 1
0.1%

Color_encoded
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.2 KiB
2
1407 
1
 
70
0
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1537
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 1407
91.5%
1 70
 
4.6%
0 60
 
3.9%

Length

2025-06-18T18:23:23.599720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T18:23:23.668511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1407
91.5%
1 70
 
4.6%
0 60
 
3.9%

Most occurring characters

ValueCountFrequency (%)
2 1407
91.5%
1 70
 
4.6%
0 60
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1407
91.5%
1 70
 
4.6%
0 60
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1407
91.5%
1 70
 
4.6%
0 60
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1407
91.5%
1 70
 
4.6%
0 60
 
3.9%

Species_encoded
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.2 KiB
0
1527 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1537
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1527
99.3%
1 10
 
0.7%

Length

2025-06-18T18:23:23.755244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T18:23:23.820645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1527
99.3%
1 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1527
99.3%
1 10
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1527
99.3%
1 10
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1527
99.3%
1 10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1527
99.3%
1 10
 
0.7%

Total Weight (g)_zscore
Real number (ℝ)

High correlation 

Distinct138
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73711578
Minimum0.0046564549
Maximum5.1934605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-06-18T18:23:23.918883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0046564549
5-th percentile0.040441311
Q10.25515045
median0.60368609
Q30.99731951
95-th percentile1.9012538
Maximum5.1934605
Range5.1888041
Interquartile range (IQR)0.74216906

Descriptive statistics

Standard deviation0.67598642
Coefficient of variation (CV)0.91706952
Kurtosis8.2876796
Mean0.73711578
Median Absolute Deviation (MAD)0.35784856
Skewness2.3169505
Sum1132.947
Variance0.45695763
MonotonicityNot monotonic
2025-06-18T18:23:24.059344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03112840093 63
 
4.1%
0.3174072475 61
 
4.0%
0.2458375358 60
 
3.9%
0.3889769591 58
 
3.8%
0.4605466707 48
 
3.1%
0.1120110223 47
 
3.1%
0.5321163824 47
 
3.1%
0.1742678242 44
 
2.9%
0.6752558057 44
 
2.9%
0.1026981126 43
 
2.8%
Other values (128) 1022
66.5%
ValueCountFrequency (%)
0.004656454889 4
 
0.3%
0.03112840093 63
4.1%
0.04044131071 35
2.3%
0.06691325675 2
 
0.1%
0.07622616653 6
 
0.4%
0.1026981126 43
2.8%
0.1120110223 47
3.1%
0.1384829684 7
 
0.5%
0.1477958782 5
 
0.3%
0.1742678242 44
2.9%
ValueCountFrequency (%)
5.193460549 1
 
0.1%
5.050321125 1
 
0.1%
4.585118 1
 
0.1%
4.370408865 2
0.1%
4.334624009 4
0.3%
4.298839153 1
 
0.1%
3.976775451 2
0.1%
3.905205739 1
 
0.1%
3.762066316 2
0.1%
3.547357181 1
 
0.1%

Total Weight (g)_is_outlier
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1510 
True
 
27
ValueCountFrequency (%)
False 1510
98.2%
True 27
 
1.8%
2025-06-18T18:23:24.145185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Total Length (cm)_zscore
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7673505
Minimum0.0030437562
Maximum5.3828445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-06-18T18:23:24.242251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0030437562
5-th percentile0.19352151
Q10.19960902
median0.59273955
Q30.98587008
95-th percentile1.7721311
Maximum5.3828445
Range5.3798007
Interquartile range (IQR)0.78626106

Descriptive statistics

Standard deviation0.64143659
Coefficient of variation (CV)0.83591082
Kurtosis6.5458559
Mean0.7673505
Median Absolute Deviation (MAD)0.39313053
Skewness1.8418418
Sum1179.4177
Variance0.41144089
MonotonicityNot monotonic
2025-06-18T18:23:24.385090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1996090204 275
17.9%
0.193521508 220
14.3%
0.5866520364 178
11.6%
0.5927395488 164
10.7%
0.9858700772 151
9.8%
0.9797825648 100
 
6.5%
1.379000606 95
 
6.2%
1.372913093 55
 
3.6%
1.766043622 40
 
2.6%
1.772131134 38
 
2.5%
Other values (69) 221
14.4%
ValueCountFrequency (%)
0.003043756206 14
 
0.9%
0.03626929664 3
 
0.2%
0.04235680905 1
 
0.1%
0.07558234948 3
 
0.2%
0.08166986189 2
 
0.1%
0.1148954023 1
 
0.1%
0.1209829147 5
 
0.3%
0.1602959676 2
 
0.1%
0.193521508 220
14.3%
0.1996090204 275
17.9%
ValueCountFrequency (%)
5.382844483 1
0.1%
4.989713955 2
0.1%
4.51795732 1
0.1%
4.321392056 1
0.1%
4.124826792 2
0.1%
3.810322369 1
0.1%
3.535130999 2
0.1%
3.25993963 1
0.1%
3.142000471 2
0.1%
2.945435207 2
0.1%

Total Length (cm)_is_outlier
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1524 
True
 
13
ValueCountFrequency (%)
False 1524
99.2%
True 13
 
0.8%
2025-06-18T18:23:24.472078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

weight_per_cm_zscore
Real number (ℝ)

High correlation 

Distinct377
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7048605
Minimum0.0037207488
Maximum8.1056689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2025-06-18T18:23:24.568985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0037207488
5-th percentile0.048254731
Q10.22417018
median0.49579536
Q30.94850401
95-th percentile1.9099218
Maximum8.1056689
Range8.1019481
Interquartile range (IQR)0.72433383

Descriptive statistics

Standard deviation0.70957682
Coefficient of variation (CV)1.0066911
Kurtosis18.462357
Mean0.7048605
Median Absolute Deviation (MAD)0.30180576
Skewness3.1289509
Sum1083.3706
Variance0.50349926
MonotonicityNot monotonic
2025-06-18T18:23:24.717437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3448924812 90
 
5.9%
0.2587190453 44
 
2.9%
0.2241701759 43
 
2.8%
0.1034478706 42
 
2.7%
0.01564983036 32
 
2.1%
0.8623305719 31
 
2.0%
0.9485040078 29
 
1.9%
0.7976011261 29
 
1.9%
0.2038452702 29
 
1.9%
0.05751520316 25
 
1.6%
Other values (367) 1143
74.4%
ValueCountFrequency (%)
0.003720748807 1
 
0.1%
0.01564983036 32
2.1%
0.01727443473 21
1.4%
0.0265607659 1
 
0.1%
0.03236472289 1
 
0.1%
0.04308671793 21
1.4%
0.04954673416 1
 
0.1%
0.05745842094 2
 
0.1%
0.05751520316 25
1.6%
0.06083999812 1
 
0.1%
ValueCountFrequency (%)
8.10566889 1
0.1%
6.381064529 1
0.1%
6.294834311 1
0.1%
6.183054398 1
0.1%
5.087611258 1
0.1%
4.483999731 1
0.1%
4.363277426 1
0.1%
4.242555121 1
0.1%
4.081592047 2
0.1%
4.00111051 1
0.1%

weight_per_cm_is_outlier
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1518 
True
 
19
ValueCountFrequency (%)
False 1518
98.8%
True 19
 
1.2%
2025-06-18T18:23:24.810123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

PCA1
Real number (ℝ)

High correlation 

Distinct439
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6925381 × 10-15
Minimum-47.294465
Maximum145.55058
Zeros0
Zeros (%)0.0%
Negative907
Negative (%)59.0%
Memory size12.1 KiB
2025-06-18T18:23:24.906352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-47.294465
5-th percentile-36.147319
Q1-18.957252
median-4.8863151
Q313.175406
95-th percentile53.326541
Maximum145.55058
Range192.84504
Interquartile range (IQR)32.132658

Descriptive statistics

Standard deviation28.08382
Coefficient of variation (CV)3.6507872 × 1015
Kurtosis3.221821
Mean7.6925381 × 10-15
Median Absolute Deviation (MAD)15.129003
Skewness1.4054939
Sum1.1461054 × 10-11
Variance788.70095
MonotonicityNot monotonic
2025-06-18T18:23:25.057369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.889222116 43
 
2.8%
-6.887768601 42
 
2.7%
-10.89067563 42
 
2.7%
-0.8279494231 32
 
2.1%
-25.02564857 29
 
1.9%
3.173009387 29
 
1.9%
-12.89212915 23
 
1.5%
-18.95725181 23
 
1.5%
-14.95196362 22
 
1.4%
-2.884861571 21
 
1.4%
Other values (429) 1231
80.1%
ValueCountFrequency (%)
-47.29446538 2
 
0.1%
-46.25334588 1
 
0.1%
-45.28229665 7
0.5%
-45.21320048 2
 
0.1%
-44.17380441 1
 
0.1%
-43.27012793 1
 
0.1%
-43.2046035 4
0.3%
-43.17019279 2
 
0.1%
-43.09917337 1
 
0.1%
-42.18018845 1
 
0.1%
ValueCountFrequency (%)
145.5505764 1
0.1%
141.5874089 1
0.1%
128.4481137 1
0.1%
122.3505585 1
0.1%
122.3460874 1
0.1%
121.7978584 1
0.1%
121.7344603 1
0.1%
121.5540166 1
0.1%
121.5145247 1
0.1%
120.4780334 1
0.1%

PCA2
Real number (ℝ)

High correlation 

Distinct439
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.610775 × 10-16
Minimum-12.475586
Maximum9.0967015
Zeros0
Zeros (%)0.0%
Negative762
Negative (%)49.6%
Memory size12.1 KiB
2025-06-18T18:23:25.206320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-12.475586
5-th percentile-1.4960243
Q1-0.57345242
median0.0048948261
Q30.63235292
95-th percentile1.6230047
Maximum9.0967015
Range21.572288
Interquartile range (IQR)1.2058053

Descriptive statistics

Standard deviation1.3011573
Coefficient of variation (CV)1.9682372 × 1015
Kurtosis20.29301
Mean6.610775 × 10-16
Median Absolute Deviation (MAD)0.58280709
Skewness-1.340549
Sum1.005418 × 10-12
Variance1.6930104
MonotonicityNot monotonic
2025-06-18T18:23:25.349708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1428460395 43
 
2.8%
-0.04094239654 42
 
2.7%
0.3266344755 42
 
2.7%
0.5046497736 32
 
2.1%
-0.475899819 29
 
1.9%
0.1517903206 29
 
1.9%
0.5104229115 23
 
1.5%
0.004894826146 23
 
1.5%
-0.3806700023 22
 
1.4%
-0.4085192685 21
 
1.4%
Other values (429) 1231
80.1%
ValueCountFrequency (%)
-12.47558632 1
0.1%
-10.34840951 1
0.1%
-10.31597926 1
0.1%
-9.490601052 1
0.1%
-8.432870413 1
0.1%
-6.841114528 1
0.1%
-6.657326092 1
0.1%
-6.433035064 1
0.1%
-6.28974922 1
0.1%
-6.105960784 1
0.1%
ValueCountFrequency (%)
9.096701501 1
0.1%
9.093560567 1
0.1%
7.813371149 1
0.1%
7.237945099 1
0.1%
6.937710506 1
0.1%
5.245804757 1
0.1%
4.897489544 1
0.1%
4.208824221 1
0.1%
3.803292545 1
0.1%
3.342991267 1
0.1%

Interactions

2025-06-18T18:23:20.361015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:13.296987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.280326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:15.303932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.583209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.482872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.337503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.445514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.470201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:13.417695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.412381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:15.644225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.701980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.595227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.473145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.570842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.574362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:13.536180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.536993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:15.790379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.812482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.709901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.588433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.690621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.676968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:13.653611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.672305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:15.916647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.925949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.817001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.887356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.808453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.777632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:13.782893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.791889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.053493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.028676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.914384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.995711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.917845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.874307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:13.903392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.908032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.181412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.130501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.011449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.105386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.027342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.979951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.034516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:15.046558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.337332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.242389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.124303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.219153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.145833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:21.084002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:14.164969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:15.182003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:16.476895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:17.383131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:18.237545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:19.337908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T18:23:20.258746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-18T18:23:25.476393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ColorColor_encodedDefectPCA1PCA2SpeciesSpecies_encodedTotal Length (cm)Total Length (cm)_is_outlierTotal Length (cm)_zscoreTotal Weight (g)Total Weight (g)_is_outlierTotal Weight (g)_zscorenum_defectsweight_per_cmweight_per_cm_is_outlierweight_per_cm_zscore
Color1.0001.0000.5200.2610.0000.0970.0970.2990.2160.2800.2630.2800.2530.2630.2190.0000.193
Color_encoded1.0001.0000.5200.2610.0000.0970.0970.2990.2160.2800.2630.2800.2530.2630.2190.0000.193
Defect0.5200.5201.0000.2440.3460.5960.5960.2820.3310.2480.2430.3420.2160.9910.2580.2200.222
PCA10.2610.2610.2441.0000.2810.2480.2480.9220.733-0.1291.0000.915-0.0570.2210.9620.433-0.062
PCA20.0000.0000.3460.2811.0000.4730.4730.5600.422-0.2150.2660.311-0.2340.0520.0620.848-0.245
Species0.0970.0970.5960.2480.4731.0000.9500.2920.0260.2860.2480.0780.1840.4520.1860.0100.119
Species_encoded0.0970.0970.5960.2480.4730.9501.0000.2920.0260.2860.2480.0780.1840.4520.1860.0100.119
Total Length (cm)0.2990.2990.2820.9220.5600.2920.2921.0000.997-0.1260.9160.706-0.0670.2120.8040.090-0.070
Total Length (cm)_is_outlier0.2160.2160.3310.7330.4220.0260.0260.9971.0000.9500.7330.5550.7520.1360.3170.0000.349
Total Length (cm)_zscore0.2800.2800.248-0.129-0.2150.2860.286-0.1260.9501.000-0.1270.6790.8050.145-0.1270.1360.606
Total Weight (g)0.2630.2630.2431.0000.2660.2480.2480.9160.733-0.1271.0000.915-0.0570.2210.9670.439-0.063
Total Weight (g)_is_outlier0.2800.2800.3420.9150.3110.0780.0780.7060.5550.6790.9151.0000.9300.1890.5670.3200.606
Total Weight (g)_zscore0.2530.2530.216-0.057-0.2340.1840.184-0.0670.7520.805-0.0570.9301.0000.164-0.0580.4350.895
num_defects0.2630.2630.9910.2210.0520.4520.4520.2120.1360.1450.2210.1890.1641.0000.2230.0370.119
weight_per_cm0.2190.2190.2580.9620.0620.1860.1860.8040.317-0.1270.9670.567-0.0580.2231.0000.997-0.065
weight_per_cm_is_outlier0.0000.0000.2200.4330.8480.0100.0100.0900.0000.1360.4390.3200.4350.0370.9971.0000.950
weight_per_cm_zscore0.1930.1930.222-0.062-0.2450.1190.119-0.0700.3490.606-0.0630.6060.8950.119-0.0650.9501.000

Missing values

2025-06-18T18:23:21.273980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-18T18:23:21.484367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ColorSpeciesTotal Length (cm)Total Weight (g)DefectImageDefect_Listnum_defectsis_skinlessis_headlessweight_per_cmColor_encodedSpecies_encodedTotal Weight (g)_zscoreTotal Weight (g)_is_outlierTotal Length (cm)_zscoreTotal Length (cm)_is_outlierweight_per_cm_zscoreweight_per_cm_is_outlierPCA1PCA2
0MediumLoligo7.115.0['Skinless', 'Headless']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/67a80077-dfa4-4516-8584-e89b8085a296.jpg[Skinless, Headless]2FalseFalse2.112676201.283598False1.339688False1.484103False-36.107473-0.271648
1DarkLoligo10.138.0['Headless']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/a6d22880-89a2-4ae9-b35c-253a7973beef.jpg[Headless]1FalseFalse3.762376000.460547False0.160296False0.488325False-12.8861640.616949
2MediumLoligo9.340.0['Whole', 'Skinless']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/610d3a5e-cd00-4e12-bf43-2e2c75ad7faf.jpg[Whole, Skinless]2FalseFalse4.301075200.388977False0.474800False0.163160False-10.930413-0.434312
3DarkLoligo12.077.0['Whole']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/7e4a52df-9749-4365-8c05-7cf53f79ace9.jpg[Whole]1FalseFalse6.416667000.935063False0.586652False1.113835False26.227081-0.728070
4DarkLoligo10.029.0['Skinless', 'Headless']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/ad034d43-94a2-4e72-905a-a74a8270d4a5.jpg[Skinless, Headless]2FalseFalse2.900000000.782610False0.199609False1.008865False-21.8986701.337471
5DarkLoligo9.046.0['Whole']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/499140b1-5b36-4959-b10d-82d1d2534ed6.jpg[Whole]1FalseFalse5.111111000.174268False0.592740False0.325787False-4.938743-1.344582
6DarkLoligo8.221.0['Headless', 'Skinless']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/af144238-8d06-4c43-9794-b307c17662cd.jpg[Headless, Skinless]2FalseFalse2.560976001.068889False0.907244False1.213504False-30.0234470.242673
7MediumLoligo9.542.0['Whole']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/9024836d-abf9-43bb-bc8f-2c0c20c10946.jpg[Whole]1FalseFalse4.421053200.317407False0.396174False0.090740False-8.917285-0.403115
8MediumLoligo13.574.0['Whole']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/a88069e0-007a-4344-be0a-b81c00f4c549.jpg[Whole]1FalseFalse5.481481200.827708False1.176348False0.549347False23.3105871.174175
9MediumLoligo11.597.0['Whole']https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/4/tallyvision_qc/d4402c50-db89-4631-a819-eb3b4780765c.jpg[Whole]1FalseFalse8.434783201.650760False0.390087False2.331993False46.202681-3.029781
ColorSpeciesTotal Length (cm)Total Weight (g)DefectImageDefect_Listnum_defectsis_skinlessis_headlessweight_per_cmColor_encodedSpecies_encodedTotal Weight (g)_zscoreTotal Weight (g)_is_outlierTotal Length (cm)_zscoreTotal Length (cm)_is_outlierweight_per_cm_zscoreweight_per_cm_is_outlierPCA1PCA2
1527MediumLoligo11.044.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/b19d9fa4-dbb6-4a35-8f3d-1614f9c6ea99.jpg[Whole, Skinless]2FalseFalse4.000000200.245838False0.193522False0.344892False-6.8293881.033939
1528MediumLoligo9.032.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/f3888a8b-587c-4d0e-a34e-d46fe533083e.jpg[Whole, Skinless]2FalseFalse3.555556200.675256False0.592740False0.613164False-18.9572520.004895
1529MediumLoligo10.040.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/dbcdd8e1-7f08-4c92-b496-de314dce8400.jpg[Whole, Skinless]2FalseFalse4.000000200.388977False0.199609False0.344892False-10.8906760.326634
1530MediumLoligo9.028.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/ff728081-5aa3-4f97-8c5b-d2d50a779449.jpg[Whole, Skinless]2FalseFalse3.111111200.818395False0.592740False0.881436False-22.9625400.390460
1531MediumLoligo13.062.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/bef0ee69-b000-4898-8b1a-db773f57b793.jpg[Whole, Skinless]2FalseFalse4.769231200.398290False0.979783False0.119424False11.2880921.623005
1532MediumLoligo10.038.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/05d3c67d-267a-43dd-bfcb-576000f65192.jpg[Whole, Skinless]2FalseFalse3.800000200.460547False0.199609False0.465615False-12.8921290.510423
1533MediumLoligo9.024.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/cc73366c-930b-4023-b865-6911dcbfd829.jpg[Whole, Skinless]2FalseFalse2.666667200.961535False0.592740False1.149708False-26.9678280.776024
1534MediumLoligo8.026.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/94aaf317-2960-459e-8a21-ea5958b9a99d.jpg[Whole, Skinless]2FalseFalse3.250000200.889965False0.985870False0.797601False-25.025649-0.475900
1535MediumLoligo8.026.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/764cda67-8a52-47e2-9efc-b6b44700780a.jpg[Whole, Skinless]2FalseFalse3.250000200.889965False0.985870False0.797601False-25.025649-0.475900
1536MediumLoligo10.042.0["Whole","Skinless"]https://storage.googleapis.com/upload-raw-images/tallyvision-camera-08/2025/4/15/tallyvision_qc/e992d5d5-10b3-4a1d-84c4-c6551b794866.jpg[Whole, Skinless]2FalseFalse4.200000200.317407False0.199609False0.224170False-8.8892220.142846